Google Inc.

Daniel Raynaud

Daniel Cotting

Boris Bluntschli

A system and computer implemented method for detecting the location of a mobile device using semantic indicators is provided. The method includes receiving, using one or more processors, a plurality of images captured by a mobile device at an area. The area is associated with a set of candidate locations. Using the one or more processors, one or more feature indicators associated with the plurality of images are detected. These feature indicators include semantic features related to the area. The semantic features are compared with a plurality of stored location features for the set of candidate locations. In accordance with the comparison, a location from the set of candidate locations is selected to identify an estimated position of the mobile device.

Detecting the location of a mobile device based on semantic indicators

According to various aspects of the subject technology, a user's personality profile is based on a user's social actions, including uploading a photo in association with a post (e.g. check-in, comment, reshare, etc.). The post's content (e.g. photo) is attributed to one or more personality dimensions in the user's personality profile. The various personality dimensions are then displayed in a compass graph, which shows user achievement on a multi-dimensional scale. Each discreet, explicit submission alters, to some degree, one or more dimensions. The compass graph is publicly viewable, and users can see the profile change over time, as each user action updates the compass graph. The dimensions can also be altered by the number of posts, time, locations of posts, subject matter (e.g. types of posts or categories of posts), and external interaction such as social interactions (e.g. feedback on posts, or positive comments).

Generating a graph for a user profile

Systems and methods for the processing of images are provided. In particular, a candidate image can be obtained for processing. The candidate image can have one or more associated image categorization parameters. One or more pixel groups can then be detected in the candidate image and the one or more pixel groups can be associated with semantic data. At least one reference image can then be identified based at least in part on the semantic data of the one or more pixel groups. Once the at least one reference image has been identified, a plurality of adjustment parameters can be determined. One or more pixel groups from the candidate image can then be processed to generate a processed image based at least in part on the plurality of adjustment parameters.

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Cupertino, CA, United States of America

Daniel Raynaud